Akshay Agrawal

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 8 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

Whom Akshay Agrawal trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Stephen P. Boyd
  2. Michael Grant
  3. Justin Domke
  4. Steven Diamond
  5. Brandon Amos
  6. Thomas Pock
  7. Erling D. Andersen
  8. Geoffrey E. Hinton
  9. Dong C. Liu
  10. Andreas Griewank

Who trusts Akshay Agrawal overall

  1. Audrey Huang
  2. Valentina Zantedeschi
  3. Sanjeev Arora
  4. Chi Zhang
  5. Max B. Paulus
  6. Brandon Amos
  7. Nan Jiang
  8. Minsik Cho
  9. Woosuk Kwon
  10. Zhenyi Wang

Papers from Akshay Agrawal's perspective

Ordered by how strongly the researchers Akshay Agrawal trusts endorse each paper. Same corpus, a different order for every researcher.

  1. ImageNet classification with deep convolutional neural networks
  2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  4. ImageNet: A large-scale hierarchical image database
  5. Gradient-based learning applied to document recognition
  6. Long Short-Term Memory
  7. Attention is All you Need (Neural Information Processing Systems)
  8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  9. Learning Multiple Layers of Features from Tiny Images
  10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers